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Multi-modal Medical SAM: An Adaptation Method of Segment Anything Model (SAM) for Glioma Segmentation Using Multi-modal MR Images

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ACM Transactions on Computing for Healthcare 2025年第2期6卷

作者： Shi, Xiaoyu Jain, Rahul Kumar Li, Yinhao Chai, Shurong Cheng, Jingliang Bai, Jie Zhao, Guohua Lin, Lanfen Chen, Yen-Wei Graduate School of Information Science and Engineering Ritsumeikan University Ibaraki Japan The Affiliated Hospital of Zhengzhou University Zhengzhou University Zhengzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Zhejiang Key Laboratory of Multi-omics Precision Diagnosis Treatment of Liver Diseases Hangzhou China

The segmentation of glioma is crucial for early diagnosis, according to a World Health Organization (WHO) 2021 report. For glioma diagnosis, 3D multi-modal brain MRI/CT imaging has become an essential tool, offering detailed information. Nowadays, deep learning frameworks have been applied to various medical imaging problems, including brain glioma segmentation. Recently, foundation models like Segment Anything Model (SAM) have emerged as pivotal tools in computer vision tasks. These models are trained using large (real-world) datasets, offering a generalized understanding of visual data and semantic key features. Therefore, the effective utilization of foundation models in medical imaging is a significant area of current research. However, the differences in data distribution between multi-modal medical images and real-world images present challenges in directly applying foundation models to medical imaging. Additionally, utilizing multi-modal images to extract crucial information and its fusion poses further challenges. To address these issues, we propose a framework using foundation model and novel strategies for multi-modal fusion. Our fusion adapters effectively integrate the information from different modalities to enhance glioma segmentation in multi-modal MRI scans. Our method outperforms current © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantic Segmentation

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CompAct: Designing Interconnected Compliant Mechanisms with Targeted Actuation Transmissions 25

CompAct: Designing Interconnected Compliant Mechanisms with ...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Humphrey Yang I-Chao Shen Nikolas Martelaro Bo Zhu Haoran Xie Takeo Igarashi Lining Yao Human-Computer Interaction Institute Carnegie Mellon University Pittsburgh Pennsylvania USA The University of Tokyo Graduate School of Information Science and Technology Tokyo Japan Georgia Institute of Technology Atlanta Georgia USA JAIST Ishikawa Japan The University of Tokyo Tokyo Japan Mechanical Engineering Department University of California Berkeley Berkeley California USA

ISBN: (纸本)9798400713941

Compliant mechanisms enable the creation of compact and easy-to-fabricate devices for tangible interaction. This work explores interconnected compliant mechanisms consisting of multiple joints and rigid bodies to transmit and process displacements as signals that result from physical interactions. As these devices are difficult to design due to their vast and complex design space, we developed a graph-based design algorithm and computational tool to help users program and customize such computational functions and procedurally model physical designs. When combined with active materials with actuation and sensing capabilities, these devices can also render and detect haptic interaction. Our design examples demonstrate the tool's capability to respond to relevant HCI concepts, including building modular physical interface toolkits, encrypting tangible interactions, and customizing user augmentation for accessibility. We believe the tool will facilitate the generation of new interfaces with enriched affordance.

关键词： Tangible interface

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Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

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Multi-Objective Mobile Damped Wave Algorithm (MOMDWA): A Novel Approach For Quantum System Control

arXiv

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arXiv 2025年

作者： Yu, Juntao Yu, Jiaquan Wei, Dedai Sha, Xinye Fu, Shengwei Qiu, Minyu Jin, Yurun OuYang, Kaichen Department of Mathematics University of Science and Technology of China Hefei230026 China College of Economics Shenyang University Shenyang110000 China Graduate School of Arts and Sciences Columbia University NY10027 United States Key Laboratory of Advanced Manufacturing Technology Ministry of Education Guizhou University Guiyang550025 China China Jiliang University Information Engineering College Zhejiang Hangzhou China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

In this paper, we introduce a novel multi-objective optimization algorithm, the Multi-Objective Mobile Damped Wave Algorithm (MOMDWA), specifically designed to address complex quantum control problems. Our approach extends the capabilities of the original Mobile Damped Wave Algorithm (MDWA) by incorporating multiple objectives, enabling a more comprehensive optimization process. We applied MOMDWA to three quantum control scenarios, focusing on optimizing the balance between control fidelity, energy consumption, and control smoothness. The results demonstrate that MOMDWA significantly enhances quantum control efficiency and robustness, achieving high fidelity while minimizing energy use and ensuring smooth control pulses. This advancement offers a valuable tool for quantum computing and other domains requiring precise, multi-objective control. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum computers

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The Models of Granular System and Algebraic Quotient Space in Granular Computing

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Chinese Journal of Electronics 2016年第6期25卷 1109-1113页

作者： CHEN Linshu WANG Jiayang LI Li School of Information Science and Engineering Central South University School of Computer Science and Engineering Hunan University of Science and Technology Harbin Institute of Technology Shenzhen Graduate School

Granular computing(Gr C) is an emerging computing paradigm, and it is an umbrella term exploring multilevel granularity. we present a generic abstract mathematical model of the granular system. Supposing the inter-granule structure as an algebra, we propose the algebraic quotient space model. In this model, the granulation is based on a congruence relation and all the congruence relations on a granular system form a complete semi-order lattice, which is the theoretical basis for transformation,composition and decomposition among different granularities. The different granulation rules between the topological quotient space model and the algebraic quotient space model lead to the dissimilarity while composing granularities. A real-world case study is presented that demonstrates how the algebraic quotient space model works in the network transmission by error-correcting code. These work shows that the granular system model and the algebraic quotient space model are powerful conceptual modeling and functional specification methodologies for Gr C.

关键词： Granular computing(GrC) Granular system Algebraic quotient space Congruence relation

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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InstaSight: Leveraging AI to See Beyond Sight for Blind Users on Social Media

InstaSight: Leveraging AI to See Beyond Sight for Blind User...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Chun-Nan Chi Jia-Lien Hsu Chi-Huang Hung Chia-Hsun Hsu To Nguyen Tan Phuong Wei-Chih Juan Graduate Institute of Applied Science and Engineering Fu Jen Catholic University New Taipei City Taiwan R.O.C. Department of Computer Science and Information Engineering Fu Jen Catholic University New Taipei City Taiwan R.O.C. Dept. of Information Technology Graduate Institute of Applied Science and Engineering Fu Jen Catholic University Lee-Ming Institute of Technology New Taipei City Taiwan R.O.C. Dept. of International High School Kang Chiao International School New Taipei City Taiwan R.O.C. Dept. Electronical Engineering Lee-Ming Institute of Technology New Taipei City Taiwan R.O.C. Department of Information Management Hwa Hsia University Of Technology New Taipei City Taiwan R.O.C.

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

This study introduces “InstaSight,” a system enabling visually impaired individuals to engage with social media focused on visual content. InstaSight integrates a Single Board computer, head-mounted webcam, and Bluetooth headset for seamless voice interactions. By utilizing the APIs of social media, speech recognition, and Large Language Models (LLMs), users can post photos and videos with voice-to-text captions and listen to comments converted from text or images. This system fosters social engagement, helping blind users connect and share experiences, improving their psychological well-being.

关键词： Headphones Visualization Electric potential Social networking (online) Webcams Large language models Psychology Speech recognition Consumer electronics Videos

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A continuous analytic channel potential solution to doped symmetric double-gate MOSFETs from the accumulation to the strong-inversion region

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Chinese Physics B 2011年第1期20卷 501-506页

作者：何进刘峰周幸叶张健张立宁 School of Electronics and Information Nantong University The Key Laboratory of Integrated Microsystems School of Computer & Information EngineeringPeking University Shenzhen Graduate School TSRC Institute of MicroelectronicsSchool of Electronic Engineering and Computer SciencePeking University

A continuous yet analytic channel potential solution is proposed for doped symmetric double-gate （DG） MOSFETs from the accumulation to the strong-inversion region. Analytical channel potential relationship is derived from the complete 1-D Poisson equation physically, and the channel potential solution of the DG MOSFET is obtained analytically. The extensive comparisons between the presented solution and the numerical simulation illustrate that the solution is not only accurate and continuous in the whole operation regime of DG MOSFETs, but also valid to wide doping concentration and various geometrical sizes, without employing any fitting parameter.

关键词： MOSFETs transistors doping modeling double-gate （DG）

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SuperFlow:A Reliable and Scalable Architecture for Large-Scale Enterprise Networks

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Chinese Journal of Electronics 2016年第6期25卷 1134-1140页

作者： HU Guangwu JIANG Yong CHEN Wenlong CHEN Tan WU Jianping School of Computer Science Shenzhen Institute of Information Technology Graduate School at Shenzhen Tsinghua University Information Engineering College Capital Normal University College of Computer Science Beijing University of Technology Department of Computer Science and Technology Tsinghua University

With the increasing number of users, enterprise networks have become more and more important, but it also faced new challenges in various *** OpenF low, the promising de facto Software defined networking(SDN) scheme which can provide finegrained and flow-level control for enterprise networks, yet it still has a few undesirable designs in credibility and scalability. Inspired by OpenF low-included many excellent studies, we have proposed a reliable and scalable architecture—Super Flow for large-scale enterprise networks in this paper. It inherits the merits of OpenF low, and overcomes OpenF lows limitations by introducing some novel *** prototype experiment has proved that Super Flow possesses these features with desirable performance.

关键词： Enterprise network SuperFlow OpenFlow Source address validation improvement(SAVI) Software defined networking(SDN)

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Efficient spin-orbit torque driven magnetization switching of GdFe using phosphorus-implanted platinum layers

arXiv

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arXiv 2025年

作者： Shintaku, Kazuki Mathew, Arun Jacob Iwamoto, Akihisa Mohammadi, Mojtaba Awano, Hiroyuki Asada, Hironori Fukuma, Yasuhiro Department of Physics and Information Technology Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka820-8502 Japan Memory Engineering Laboratory Toyota Technological Institute Nagoya468-8511 Japan Graduate School of Sciences and Technology for Innovation Yamaguchi University Ube755-8611 Japan Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu808-0196 Japan

The capability of the spin-orbit torque (SOT) generated via phenomena such as the spin Hall effect in heavy metals, in switching the magnetization of an adjacent magnetic material, has been studied extensively over the last decade. The efficiency of SOT generation is commonly quantified in terms of the spin Hall angle θSH. In this work, we demonstrate experimentally that implanting platinum (Pt) with phosphorus (P), resulting in Pt (P) d, where d denotes the implantation dose, increases θSH by a factor of 7, from 0.06 (d = 0) to 0.43 (d = 10 × 1016 ions/cm2). The enhanced θSH along with factors such as perpendicular magnetic anisotropy and resistivity, lead to reduction of the critical current density for switching the perpendicular magnetization of ferrimagnetic rare earth-transition metal alloy Gd26Fe74, by a factor of nearly 27, from 4.0 × 1011 A/m2 (d = 0) to 1.5 × 1010 A/m2 (d = 10 × 1016 ions/cm2). Further, the switching current density at zero thermal fluctuations and thermal stability factor were evaluated and found to be 2.0 × 1010 A/m2 and 61.4 (d = 10 × 1016 ions/cm2), with the latter being sufficiently above the required threshold for commercial memory applications. Our results suggest that Pt (P) could be a strong candidate in realizing efficient SOT driven magnetization switching leading to the development of improved memory and logic devices in the future. Copyright © 2025, The Authors. All rights reserved.

关键词： Iron alloys

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